Managing a set of compute nodes which have different configurations in a stream computing environment

ABSTRACT

Disclosed aspects relate to managing a set of compute nodes for processing a stream of tuples using a set of processing elements. The set of compute nodes is structured to include both a first compute node having a first configuration and a second compute node having a second configuration. The first configuration differs from the second configuration. Based on the first configuration and the set of processing elements which includes a first processing element, a determination is made to establish the first processing element on the first compute node and the first processing element is established on the first compute node. In embodiments, based on the second configuration and the set of processing elements which includes a second processing element, a determination is made to establish the second processing element on the second compute node and the second processing element is established on the second compute node.

BACKGROUND

This disclosure relates generally to computer systems and, moreparticularly, relates to managing a set of compute nodes which havedifferent configurations in a stream computing environment. The amountof stream computing data that needs to be managed by enterprises isincreasing. Management of compute nodes in stream computing environmentsmay be desired to be performed as efficiently as possible. As streamcomputing data needing to be managed increases, the need for managementefficiency may increase.

SUMMARY

Aspects of the disclosure relate to managing and supporting streamcomputing/processing application execution across a set of compute nodeswith different configurations. Various performance or efficiencybenefits may result from running such applications on heterogeneouscompute nodes rather than simply homogeneous compute nodes. For example,the different configurations may include different operating systems,different versions of operating systems, different hardwarearchitectures, different algorithms based on computing capabilities ofthe respective compute nodes, different application bundles, or thelike. The different configurations may provide performance or efficiencybenefits for running stream computing/processing applications in cloudcomputing environments.

Aspects of the disclosure relate to managing a set of compute nodes forprocessing a stream of tuples using a set of processing elements. Theset of compute nodes is structured to include both a first compute nodehaving a first configuration and a second compute node having a secondconfiguration. The first configuration differs from the secondconfiguration. In embodiments, the different configurations may includedifferent operating systems, different versions of operating systems,different hardware architectures, different algorithms based oncomputing capabilities of the respective compute nodes, differentapplication bundles, or the like. Accordingly, the set of compute nodescan be considered heterogeneous in nature.

Based on the first configuration and the set of processing elementswhich includes a first processing element, a determination is made toestablish the first processing element on the first compute node and thefirst processing element is established on the first compute node. Inembodiments, based on the second configuration and the set of processingelements which includes a second processing element, a determination ismade to establish the second processing element on the second computenode and the second processing element is established on the secondcompute node. Altogether, performance or efficiency benefits withrespect to managing a set of compute nodes which have differentconfigurations in a stream computing environment may occur.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 illustrates an exemplary computing infrastructure to execute astream computing application according to embodiments.

FIG. 2 illustrates a view of a compute node according to embodiments.

FIG. 3 illustrates a view of a management system according toembodiments.

FIG. 4 illustrates a view of a compiler system according to embodiments.

FIG. 5 illustrates an exemplary operator graph for a stream computingapplication according to embodiments.

FIG. 6 is a flowchart illustrating a method for managing a set ofcompute nodes for processing a stream of tuples using a set ofprocessing elements, according to embodiments.

FIG. 7 is a flowchart illustrating a method for managing a set ofcompute nodes for processing a stream of tuples using a set ofprocessing elements, according to embodiments.

FIG. 8 illustrates an example stream computing environment having a setof compute nodes which have different configurations, according toembodiments.

FIG. 9 illustrates an example stream computing environment having a setof compute nodes which have different configurations, according toembodiments.

FIG. 10 illustrates an example stream computing environment having a setof compute nodes which have different configurations, according toembodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the disclosure relate to managing and supporting streamcomputing/processing application execution across a set of compute nodeswith different configurations. Various performance or efficiencybenefits may result from running such applications on heterogeneouscompute nodes rather than simply homogeneous compute nodes. For example,the different configurations may include different operating systems,different versions of operating systems, different hardwarearchitectures, different algorithms based on computing capabilities ofthe respective compute nodes, different application bundles, or thelike. The different configurations may provide performance or efficiencybenefits for running stream computing/processing applications in cloudcomputing environments.

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream computing application, stream operators are connected to oneanother such that data flows from one stream operator to the next (e.g.,over a TCP/IP socket). When a stream operator receives data, it mayperform operations, such as analysis logic, which may change the tupleby adding or subtracting attributes, or updating the values of existingattributes within the tuple. When the analysis logic is complete, a newtuple is then sent to the next stream operator. Scalability is achievedby distributing an application across nodes by creating executables(i.e., processing elements), as well as replicating processing elementson multiple nodes and load balancing among them. Stream operators in astream computing application can be fused together to form a processingelement that is executable. Doing so allows processing elements to sharea common process space, resulting in much faster communication betweenstream operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.A particular stream operator may not reside within the same operatingsystem process as other stream operators. In addition, stream operatorsin the same operator graph may be hosted on different nodes, e.g., ondifferent compute nodes or on different cores of a compute node.

Data flows from one stream operator to another in the form of a “tuple.”A tuple is a sequence of one or more attributes associated with anentity. Attributes may be any of a variety of different types, e.g.,integer, float, Boolean, string, etc. The attributes may be ordered. Inaddition to attributes associated with an entity, a tuple may includemetadata, i.e., data about the tuple. A tuple may be extended by addingone or more additional attributes or metadata to it. As used herein,“stream” or “data stream” refers to a sequence of tuples. Generally, astream may be considered a pseudo-infinite sequence of tuples.

Tuples are received and output by stream operators and processingelements. An input tuple corresponding with a particular entity that isreceived by a stream operator or processing element, however, isgenerally not considered to be the same tuple that is output by thestream operator or processing element, even if the output tuplecorresponds with the same entity or data as the input tuple. An outputtuple need not be changed in some way from the input tuple.

Nonetheless, an output tuple may be changed in some way by a streamoperator or processing element. An attribute or metadata may be added,deleted, or modified. For example, a tuple will often have two or moreattributes. A stream operator or processing element may receive thetuple having multiple attributes and output a tuple corresponding withthe input tuple. The stream operator or processing element may onlychange one of the attributes so that all of the attributes of the outputtuple except one are the same as the attributes of the input tuple.

Generally, a particular tuple output by a stream operator or processingelement may not be considered to be the same tuple as a correspondinginput tuple even if the input tuple is not changed by the processingelement. However, to simplify the present description and the claims, anoutput tuple that has the same data attributes or is associated with thesame entity as a corresponding input tuple will be referred to herein asthe same tuple unless the context or an express statement indicatesotherwise.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.Unless the stream operators are located in the same processing element,it is necessary to use an inter-process communication path each time atuple is sent from one stream operator to another. Inter-processcommunication paths can be a critical resource in a stream computingapplication. According to various embodiments, the available bandwidthon one or more inter-process communication paths may be conserved.Efficient use of inter-process communication bandwidth can speed upprocessing.

A streams processing job has a directed graph of processing elementsthat send data tuples between the processing elements. The processingelement operates on the incoming tuples, and produces output tuples. Aprocessing element has an independent processing unit and runs on ahost. The streams platform can be made up of a collection of hosts thatare eligible for processing elements to be placed upon. When a job issubmitted to the streams run-time, the platform scheduler processes theplacement constraints on the processing elements, and then determines(the best) one of these candidates host for (all) the processingelements in that job, and schedules them for execution on the decidedhost.

Aspects of the disclosure include a method, system, and computer programproduct for managing a set of compute nodes for processing a stream oftuples using a set of processing elements. The set of compute nodes isstructured to include both a first compute node having a firstconfiguration and a second compute node having a second configuration.The first configuration differs from the second configuration. Inembodiments, the different configurations may include differentoperating systems, different versions of operating systems, differenthardware architectures, different algorithms based on computingcapabilities of the respective compute nodes, different applicationbundles, or the like. Accordingly, the set of compute nodes can beconsidered heterogeneous in nature.

Based on the first configuration and the set of processing elementswhich includes a first processing element, a determination is made toestablish the first processing element on the first compute node and thefirst processing element is established on the first compute node. Inembodiments, based on the second configuration and the set of processingelements which includes a second processing element, a determination ismade to establish the second processing element on the second computenode and the second processing element is established on the secondcompute node. Altogether, performance or efficiency benefits withrespect to managing a set of compute nodes which have differentconfigurations in a stream computing environment may occur (e.g., speed,flexibility, resource usage, productivity). Aspects may save computingresources such as bandwidth, disk, processing, or memory.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream computing application, according tosome embodiments. The computing infrastructure 100 includes a managementsystem 105 (which can include an operator graph 132 and a stream manager134) and two or more compute nodes 110A-110D—i.e., hosts—which arecommunicatively coupled to each other using one or more communicationsnetworks 120. The communications network 120 may include one or moreservers, networks, or databases, and may use a particular communicationprotocol to transfer data between the compute nodes 110A-110D. Acompiler system 102 may be communicatively coupled with the managementsystem 105 and the compute nodes 110 either directly or via thecommunications network 120.

The communications network 120 may include a variety of types ofphysical communication channels or “links.” The links may be wired,wireless, optical, or any other suitable media. In addition, thecommunications network 120 may include a variety of network hardware andsoftware for performing routing, switching, and other functions, such asrouters, switches, or bridges. The communications network 120 may bededicated for use by a stream computing application or shared with otherapplications and users. The communications network 120 may be any size.For example, the communications network 120 may include a single localarea network or a wide area network spanning a large geographical area,such as the Internet. The links may provide different levels ofbandwidth or capacity to transfer data at a particular rate. Thebandwidth that a particular link provides may vary depending on avariety of factors, including the type of communication media andwhether particular network hardware or software is functioning correctlyor at full capacity. In addition, the bandwidth that a particular linkprovides to a stream computing application may vary if the link isshared with other applications and users. The available bandwidth mayvary depending on the load placed on the link by the other applicationsand users. The bandwidth that a particular link provides may also varydepending on a temporal factor, such as time of day, day of week, day ofmonth, or season.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A stream computing application may include one or more stream operators240 that may be compiled into a “processing element” container 235. Thememory 225 may include two or more processing elements 235, eachprocessing element having one or more stream operators 240. Each streamoperator 240 may include a portion of code that processes tuples flowinginto a processing element and outputs tuples to other stream operators240 in the same processing element, in other processing elements, or inboth the same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260. Although shown as being instorage, the buffer 260 may be located in the memory 225 of the computenode 110 or in a combination of both memories. Moreover, storage 230 mayinclude storage space that is external to the compute node 110, such asin a cloud.

The compute node 110 may include one or more operating systems 262. Anoperating system 262 may be stored partially in memory 225 and partiallyin storage 230. Alternatively, an operating system may be storedentirely in memory 225 or entirely in storage 230. The operating systemprovides an interface between various hardware resources, including theCPU 205, and processing elements and other components of the streamcomputing application. In addition, an operating system provides commonservices for application programs, such as providing a time function.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. Additionally, the storage330 may store an operator graph 335. The operator graph 335 may definehow tuples are routed to processing elements 235 (FIG. 2) for processingor stored in memory 325 (e.g., completely in embodiments, partially inembodiments).

The management system 105 may include one or more operating systems 332.An operating system 332 may be stored partially in memory 325 andpartially in storage 330. Alternatively, an operating system may bestored entirely in memory 325 or entirely in storage 330. The operatingsystem provides an interface between various hardware resources,including the CPU 305, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function. Portions of stream manager 134 or operator graph 335 maybe stored in memory 325 or storage 330 at different times in variousembodiments.

FIG. 4 is a more detailed view of the compiler system 102 of FIG. 1according to some embodiments. The compiler system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thecompiler system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the compiler system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The compiler system 102 may include one or more operating systems 432.An operating system 432 may be stored partially in memory 425 andpartially in storage 430. Alternatively, an operating system may bestored entirely in memory 425 or entirely in storage 430. The operatingsystem provides an interface between various hardware resources,including the CPU 405, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe stream computing application. In some embodiments, the compiler 136may be a just-in-time compiler that executes as part of an interpreter.In other embodiments, the compiler 136 may be an optimizing compiler. Invarious embodiments, the compiler 136 may perform peepholeoptimizations, local optimizations, loop optimizations, inter-proceduralor whole-program optimizations, machine code optimizations, or any otheroptimizations that reduce the amount of time required to execute theobject code, to reduce the amount of memory required to execute theobject code, or both. The output of the compiler 136 may be representedby an operator graph, e.g., the operator graph 335.

FIG. 5 illustrates an exemplary operator graph 500 for a streamcomputing application beginning from one or more sources 135 through toone or more sinks 504, 506, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. In addition, a flow from one processing element toanother may be referred to as an execution path in various contexts.Although FIG. 5 is abstracted to show connected processing elementsPE1-PE10, the operator graph 500 may include data flows between streamoperators 240 (FIG. 2) within the same or different processing elements.Typically, processing elements, such as processing element 235 (FIG. 2),receive tuples from the stream as well as output tuples into the stream(except for a sink—where the stream terminates, or a source—where thestream begins). While the operator graph 500 includes a relatively smallnumber of components, an operator graph may be much more complex and mayinclude many individual operator graphs that may be statically ordynamically linked together.

The example operator graph shown in FIG. 5 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. Inter-process communication paths used for inter-processcommunications can be a critical resource in a stream computingapplication. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

The operator graph 500 begins at a source 135 and ends at a sink 504,506. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 135 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to PE6 and to operator sink 504. Similarly, tuplesflowing from PE3 to PE5 and to PE6 also reach the operators in sink 504.Thus, in addition to being a sink for this example operator graph, PE6could be configured to perform a join operation, combining tuplesreceived from PE4 and PE5. This example operator graph also shows tuplesflowing from PE3 to PE7 on compute node 110C, which itself shows tuplesflowing to PE8 and looping back to PE7. Tuples output from PE8 flow toPE9 on compute node 110D, which in turn outputs tuples to be processedby operators in a sink processing element, for example PE10 506.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML, documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 of FIG. 1 may be configured to monitor a streamcomputing application running on compute nodes, e.g., compute nodes110A-110D, as well as to change the deployment of an operator graph,e.g., operator graph 132. The stream manager 134 may move processingelements from one compute node 110 to another, for example, to managethe processing loads of the compute nodes 110A-110D in the computinginfrastructure 100. Further, stream manager 134 may control the streamcomputing application by inserting, removing, fusing, un-fusing, orotherwise modifying the processing elements and stream operators (orwhat tuples flow to the processing elements) running on the computenodes 110A-110D.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity.

FIG. 6 is a flowchart illustrating a method 600 for managing a set ofcompute nodes for processing a stream of tuples using a set ofprocessing elements, according to embodiments. In order for the set ofprocessing elements to be executed on the set of compute nodes, anexecutable code image for a job (application bundle) may be madeavailable to a specific compute node. Other than job submission,application bundle management may also be performed when a processingelement gets moved to a new compute node. Typical reasons for whyprocessing elements get moved to new compute nodes are forload-balancing purposes, or for failover scenarios when a host goesdown. If the new compute node does not have the application bundleavailable, then it can be provisioned there (e.g., retrieved forinstallation). Application bundles can be relatively large, so there isa measurable cost of moving/storing application bundles. Aspects of themethod 600 may substantially correspond to other embodiments describedherein, including FIGS. 1-10 and the related descriptions. Method 600may begin at block 601.

At block 610, the set of compute nodes is structured to include both afirst compute node having a first configuration and a second computenode having a second configuration. Structuring can includeestablishing, constructing, generating, creating, forming, organizing,introducing, or compiling. The first configuration differs from thesecond configuration. The different configurations can be more thanhaving different processing power capabilities. Accordingly, the set ofcompute nodes may be considered heterogeneous in nature at block 605. Assuch, stream computing/processing applications may be run/executed on acluster of systems that have mixed system typologies. The ability toconstruct the cluster to run stream applications out of a variety ofsystem types may provide flexibility (e.g., to an administrator/user).For instance, such ability may be useful in a for a shared pool ofconfigurable computing resources (e.g., a cloud-type resource managementsystem). Such a shared pool may have a variety of host types, or variousburdens/costs to using different configurations or host types.

In embodiments, the first configuration includes a first operatingsystem and the second configuration includes a second operating systemat block 611. For example, the first configuration may include a Linux(trademark of Linus Torvalds) operating system and the secondconfiguration may include a Windows (Windows is a registered trademarkof Microsoft Corporation in the United States and/or other countries)operating system. In embodiments, the first configuration includes afirst version/level of a first operating system and the secondconfiguration includes a second version/level of the first operatingsystem at block 613. For example, the first configuration can have RedHat (Red Hat is a registered trademark of Red Hat, Inc. in the UnitedStates and other countries) version 6 and the second configuration canhave Red Hat version 7.

In embodiments, the first configuration includes a first hardwarearchitecture and the second configuration includes a second hardwarearchitecture at block 615. For example, the first configuration may usea POWER (trademark of International Business Machines Corporation) orPOWER-type processor and the second configuration may use an Intel(trademark of Intel Corporation or its subsidiaries in the U.S. and/orother countries) or Intel-type processor. In embodiments, the firstconfiguration includes a first algorithm based on a first set ofcomputing capabilities of the first compute node and the secondconfiguration includes a second algorithm based on a second set ofcomputing capabilities of the second compute node at block 617. Forinstance, the first and second configurations may include differentalgorithm implementations tailored to each execution host's capabilities(e.g., bandwidth factors, disk factors, processor factors, memoryfactors, software factors).

At block 620, based on the first configuration and the set of processingelements which includes a first processing element, a determination ismade to establish the first processing element on the first computenode. Determining can include resolving, computing, or ascertaining.When a job is submitted to a streams runtime, a platform scheduler canprocess a set of placement constraints on the processing elements. Usingthe set of placement constraints, a chosen compute node (e.g., the firstcompute node) of a set of candidate compute nodes may identified andselected for the first processing element to execute a portion of thejob on the chosen compute node. Such identification and selection mayoccur using a controller on the first compute node which analyzes thefirst configuration. The controller can use such analysis to determinewhether the first processing element would be appropriate for the firstcompute node. Accordingly, the platform scheduler and the controller mayexchange information when making the determination. In embodiments, inorder for the first processing element to be executed on the firstcompute node, an executable code image (application bundle) for the joband for the first configuration is made available on the first computenode. In various embodiments, the controller can make the determinationindependent of the platform scheduler based on the various compute nodeconfigurations.

For example, a central scheduler may work with various compute nodecontrollers to determine which operators get fused into which processingelements and which processing elements get allocated to which computenodes (e.g., based on configuration information provided by the variouscompute node controllers). Constraints can be processed to determinecandidate operator sets to construct processing elements from. From thecandidate operator sets, a chosen operator set may be selected fromwhich to construct processing elements. The constraints can be processedto determine the candidate processing elements to compute nodeallocations. From the candidates, a chosen processing element to computenode allocation may be determined in accordance with configurationinformation ascertained by various compute node controllers local toindividual compute nodes.

For instance, in response to the central scheduler performing its otherscheduling phases and determining which processing elements belong onwhich compute nodes, the controller on the first compute node can thenutilize relevant characteristics of itself (e.g., the firstconfiguration) and evaluate it against the criteria of the variousvariations of processing elements that are in the repository, and selectthe one that matches the best. Accordingly, the controller on the firstcompute node (e.g., a local controller) can manage which executablevariation runs best on itself without the central scheduler needing tobe involved, aware, or keep track of each host's individualcharacteristics or configuration.

At block 630, the first processing element is established on the firstcompute node. Establishing may include initiation/commencement of adeployment, placement, installation, or allocation. The controller onthe first compute node can initiate execution of the first processingelement (e.g., and other processing elements that have been allocated tothe first compute node). An appropriate application bundle/executablefor the first configuration can be determined (by the controller). Inaccordance with the determination, the appropriate applicationbundle/executable for the first processing element for the firstconfiguration can be invoked.

At block 640, based on the second configuration and the set ofprocessing elements which includes a second processing element, adetermination may be made to establish the second processing element onthe second compute node. The methodology for the determination for thesecond processing element, second compute node, and second configurationmay be similar to or the same as the methodology for the determinationfor the first processing element, first compute node, and firstconfiguration at block 620 and as described herein. At block 650, thesecond processing element may be established on the second compute node.The methodology for the establishment of the second processing elementon the second compute node may be similar to or the same as themethodology for the establishment of the first processing element on thefirst compute node at block 630 and as described herein.

In embodiments, the first processing element has a first applicationbundle which corresponds to the first configuration and the secondprocessing element has a second application bundle which corresponds tothe second configuration at block 619. For example, as the processingelements are submitted to run on a specific compute node, the specificcompute node can use its controller (and associated self awarenessinformation) to determine which application bundle that it needs to runon itself (e.g., for that configuration/system). As the streamcomputing/processing application is running, the processing elements maybe moved to different hosts (e.g., for load balancing, for highavailability). As such, a certain processing element running on thefirst compute node of the first configuration may be moved to the secondcompute node of the second configuration (thereby needing to use adifferent application bundle). In response, the streamcomputing/processing application can continue running and carrying-outoperations (e.g., just as it was before but now on a different type ofsystem) by using/selecting an appropriate application bundle thatmatches the second configuration. In various embodiments, certainspecialized capabilities (e.g., an algorithmic implementation) may beutilized by having the controller of the second compute node select aspecialized version of the application bundle for itself that istailored to take advantage of these capabilities (e.g., a specializedhardware assist component).

Method 600 concludes at block 699. Aspects of method 600 may provideperformance or efficiency benefits for managing a stream computingenvironment. For example, aspects of method 600 may have positiveimpacts with respect to processing a stream of tuples using a set ofprocessing elements and a set of application bundles. Altogether,performance or efficiency benefits (e.g., load balancing, highavailability, error event recovery, stability, speed, computing resourceefficiency) may occur when managing a set of compute nodes which havedifferent configurations.

FIG. 7 is a flowchart illustrating a method 700 for managing a set ofcompute nodes for processing a stream of tuples using a set ofprocessing elements, according to embodiments. Aspects of the method 700may substantially correspond to other embodiments described herein,including FIGS. 1-10 and the related descriptions. The method 700 maybegin at block 701. At block 704, the operational steps such as thestructuring, the determining, and the establishing each occur in anautomated fashion without user intervention (e.g., fully machine-drivenwithout manual stimuli). At block 710, the set of compute nodes isstructured to include both a first compute node having a firstconfiguration and a second compute node having a second configuration.The first configuration differs from the second configuration. Based onthe first configuration and the set of processing elements whichincludes a first processing element, a determination is made at block720 to establish the first processing element on the first compute nodeand the first processing element is established on the first computenode at block 730.

In embodiments, based on the second configuration and the set ofprocessing elements which includes a second processing element, adetermination is made at block 720 to establish the second processingelement on the second compute node and the second processing element isestablished on the second compute node at block 730. At block 755, afirst controller of the first compute node establishes the firstprocessing element on the first compute node without usage of a streamsmanager. For instance, a local controller on a local host may handleinstantiation operations of processing elements and application bundlesto the local host without utilizing a centralizedadministration/deployment/placement engine for components of thestreaming environment.

At block 787, a stream of tuples is received. The stream of tuples maybe processed by the set of processing elements operating on the set ofcompute nodes (in a stream application environment). The stream oftuples may be received consistent with the description herein includingFIGS. 1-10. Current/future processing by the plurality of processingelements may be performed consistent with the description hereinincluding FIGS. 1-10. The set of compute nodes may include a shared poolof configurable computing resources. For example, the set of computenodes can be a public cloud environment, a private cloud environment, ora hybrid cloud environment. In certain embodiments, each of the set ofcompute nodes are physically separate from one another.

In embodiments, the stream of tuples is processed at block 788. Thestream of tuples may be processed by the set of processing elementsoperating on the set of compute nodes. The stream of tuples may beprocessed consistent with the description herein including FIGS. 1-10.In embodiments, stream operators operating on the set of compute nodesmay be utilized to process the stream of tuples. Processing of thestream of tuples by the set of processing elements may provide variousflexibilities for managing the set of compute nodes. Overall flow (e.g.,data flow) may be positively impacted by utilizing aspects describedherein.

In certain embodiments, a usage assessment may be generated with respectto the first processing element (or the second processing element or theset of processing elements). Use of the first processing element may bemetered at block 797. For example, service-life extensions may bemeasured or functional up-time relative to a benchmark (e.g., historicalfunctional up-time) can be evaluated, etc. Such factors may correlate tocharge-back or cost burdens which can be defined in-advance (e.g.,utilizing usage tiers) or scaled with respect to a market-rate. Aninvoice or bill presenting the usage, rendered services, fee, and otherpayment terms may be generated based on the metered use at block 798.The generated invoice may be provided (e.g., displayed in a dialog box,sent or transferred by e-mail, text message, initiated for traditionalmail) to the user for notification, acknowledgment, or payment.

Method 700 concludes at block 799. Aspects of method 700 may provideperformance or efficiency benefits for managing a stream computingenvironment. For example, aspects may have positive impacts with respectto processing the stream of tuples using the set of processing elementsmay be associated with performance or efficiency benefits for managing aset of compute nodes which have different configurations (e.g., speed,flexibility, resource usage, productivity).

FIG. 8 illustrates an example stream computing environment 800 having aset of compute nodes 820, 840, 860, 880 which have differentconfigurations, according to embodiments. Host # A 820 has aconfiguration of Red Hat version 7, and PE1, PE2, and PE3 can beconfigured for such (e.g., use an application bundle configured foroperation with Red Hat version 7). Host # B 840 has a configuration ofRed Hat version 6, and PE4, PE5, and PE6 can be configured for such(e.g., use an application bundle configured for operation with Red Hatversion 6). Host # C 860 has a configuration of a Windows operatingsystem on an Intel hardware processor, and PE7 and PE8 can be configuredfor such (e.g., use an application bundle configured for operation withthe Windows operating system on the Intel hardware processor). Host # D880 has a configuration of a Linux operating system on a Power hardwareprocessor, and PE9 and PE10 can be configured for such (e.g., use anapplication bundle configured for operation with the Linux operatingsystem on the Power hardware processor). Accordingly, the ability tocreate an environment running the streams application out of a varietyof system types can provide various performance or efficiency benefitssuch as flexibility. In particular, benefits may be realized incloud-type resource management systems a variety of host types may exist(e.g., with various burdens/costs to using different host types).

FIG. 9 illustrates an example stream computing environment 900 having aset of compute nodes 920, 940, 960, 980 which have differentconfigurations and may be substantially similar to the set of computenodes 820, 840, 860, 880 of the example stream computing environment800, according to embodiments. Host # A 920 has a configuration of RedHat version 7, and PE1, PE2, PE3, and PE7 can be configured for such(e.g., use an application bundle configured for operation with Red Hatversion 7). Host # B 940 has a configuration of Red Hat version 6, andPE4, PE5, and PE6 can be configured for such (e.g., use an applicationbundle configured for operation with Red Hat version 6). Host # C 960has a configuration of a Windows operating system on an Intel hardwareprocessor, and may have succumb to an error event. Host # D 980 has aconfiguration of a Linux operating system on a Power hardware processor,and PE8, PE9, and PE10 can be configured for such (e.g., use anapplication bundle configured for operation with the Linux operatingsystem on the Power hardware processor).

When comparing FIG. 8 and FIG. 9, take for example that if Host # C 860crashed, a streams management service can detect this host failure. Inresponse, the streams management service can resolve that PE7 and PE8will need to get to moved to another host. A scheduler portion of themanagement service may determine that the appropriate new host for PE7is Host # A 920, and that the appropriate new host for PE8 is Host # D980. Accordingly, PE7 and PE8 functionality may dynamically andseamlessly switch their operating environment in a streamlined fashionwithout involvement in the switch from streams management services.Accordingly, PE7 will switch from running as a Windows/Intel processingelement to a RedHat version 7 processing element and PE8 will switchfrom Windows/Intel processing element to a Linux/Power processingelement.

To illustrate, a triggering event (e.g., host failure, error event) maybe detected related to the first compute node (e.g., Host # C 860). Thefirst and second processing elements (e.g., PE7 on Host # C 860 and PE7on Host # A 920, PE8 on Host # C 860 and PE8 on Host # D 980) may be asame processing element (e.g., PE7, PE8). As such, the same processingelement may have switched application bundles which correspond to anappropriate configuration for its host on which it is running. Forinstance, the first processing element (e.g., PE7, PE8) may a firstapplication bundle which corresponds to the first configuration (e.g.,Host # C 860 with the Windows operating system on the Intel hardwareprocessor). The triggering event may be detected related to the firstcompute node (e.g., Host # C 860/960). Based on the second configuration(e.g., of Host # A 920, of Host # D 980) and the set of processingelements which includes the first processing element, it can bedetermined to establish the first processing element on the secondcompute node (e.g., PE7 on Host # A 920, PE8 on Host # D 980).Accordingly, the first processing element may be established on thesecond compute node (e.g., PE7 on Host # A 920, PE8 on Host # D 980). Asecond application bundle which corresponds to the second configuration(e.g., for Red Hat version 7 for PE7, for Linux/Power for PE8) may beinstalled on the second compute node.

FIG. 10 illustrates an example stream computing environment 1000 havinga user 1010 and a set of compute nodes 1030, 1050, 1070 which may havedifferent configurations, according to embodiments. User 1010 may submita job with a global application bundle (e.g., a super applicationbundle). The job can be received by a scheduler of Host # A 1030. Thescheduler may initiate installation of the global application bundle toan application bundle repository 1090. In embodiments, separate bundleartifacts may be sorted-out of the global application bundle for thevarious configurations. In certain embodiments, the application bundlerepository 1090 may be pre-populated directly by the compiler on anindividual bundle-by-bundle basis prior to job submission.

The global application bundle may include versions/variants of specificapplication bundles for various configurations and related to variousprocessing elements. Accordingly, a set of application bundles (e.g.,contents of the global application bundle) may be produced by a compilerfor operation within both a first and a second configurations. As such,the set of application bundles may be one (global) application bundlehaving configurations for: different operating systems, differentversions of an operating system, different hardware architectures, orthe like.

For example, the scheduler can initiate start-up of a first processingelement on Host # B 1050 using a host controller on Host # B 1050. Thehost controller on Host # B 1050 may submit a retrieve request orretrieve a particular application bundle for Red Hat version 6 and thefirst processing element from the application bundle repository 1090(e.g., a first controller of the first compute node may retrieve a firstapplication bundle). The particular application bundle for Red Hatversion 6 and the first processing element may be invoked on Host # B1050 from the application bundle repository 1090 (e.g., the firstcontroller of the first compute node may install the first applicationbundle). The scheduler may invokes a startPE operation on a particularhost by passing along an application identifier and a processing elementidentifier. The scheduler may be indifferent (e.g., unaware) withrespect to what version/variant of bundles are needed by which hosts.

Similarly, the scheduler can initiate start-up of a second processingelement on Host # C 1070 using a host controller on Host # C 1070. TheHost controller on Host # C 1070 can check to see if a particularapplication bundle for Windows and the second processing element isalready installed on Host # C 1070. In response to it not beinginstalled, the host controller on Host # C 1070 may submit a retrieverequest or retrieve the particular application bundle for Windows andthe second processing element from the application bundle repository1090. The particular application bundle for Windows and the secondprocessing element may be invoked on Host # C 1070 from the applicationbundle repository 1090. Once the particular bundle is installed, theprocessing element is started. A stream of tuples may flow from thefirst processing element on Host # B 1050 to the second processingelement on Host # C 1070.

In addition to embodiments described above, other embodiments havingfewer operational steps, more operational steps, or differentoperational steps are contemplated. Also, some embodiments may performsome or all of the above operational steps in a different order. Inembodiments, operational steps may be performed in response to otheroperational steps. The modules are listed and described illustrativelyaccording to an embodiment and are not meant to indicate necessity of aparticular module or exclusivity of other potential modules (orfunctions/purposes as applied to a specific module).

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Many modifications and variations may be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. Furthermore, although embodiments of thisdisclosure may achieve advantages over other possible solutions or overthe prior art, whether or not a particular advantage is achieved by agiven embodiment is not limiting of this disclosure. Thus, the describedaspects, features, embodiments, and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

Embodiments of the present disclosure may also be delivered as part of aservice engagement with a client corporation, nonprofit organization,government entity, internal organizational structure, or the like. Theseembodiments may include configuring a computer system to perform, anddeploying software, hardware, and web services that implement, some orall of the methods described herein. These embodiments may also includeanalyzing the client's operations, creating recommendations responsiveto the analysis, building systems that implement portions of therecommendations, integrating the systems into existing processes andinfrastructure, metering use of the systems, allocating expenses tousers of the systems, and billing for use of the systems.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow. The descriptions of the various embodiments of thepresent disclosure have been presented for purposes of illustration, butare not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen toexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method for managing a setof heterogeneous compute nodes for processing a stream of tuples using aset of processing elements, the method comprising: structuring the setof compute nodes to include both a first compute node having a firstconfiguration and a second compute node having a second configuration,wherein the first configuration differs from the second configuration;determining, by a first controller on the first compute node, based onthe first configuration and the set of processing elements whichincludes a first processing element, to establish the first processingelement on the first compute node, wherein one or more stream operatorsare utilized to form one or more processing elements; establishing thefirst processing element on the first compute node; determining, by asecond controller on the second compute node, based on the secondconfiguration and the set of processing elements which includes a secondprocessing element, to establish the second processing element on thesecond compute node; establishing the second processing element on thesecond compute node; associating the established first processingelement with a first application bundle corresponding to the firstconfiguration and the established second processing element with asecond application bundle corresponding to the second configuration;submitting the associated first processing element and associated secondprocessing element to run on a specific compute node, wherein thespecific compute node utilizes a controller associated with the specificcompute node and a plurality of self-awareness information associatedwith the specific compute node to determine an application bundleassociated with the specific compute node to run a configurationassociated with the specific compute node, wherein one or moreprocessing elements is moved to one or more different hosts as a streamcomputing application is running, wherein each of the different hostsfrom the one or more different hosts are associated with two or morespecific compute nodes; metering use of the first processing element andthe second processing element; generating an invoice based on comparingthe metered use to a benchmark; and presenting the generated invoice toa user.
 2. The method of claim 1, wherein the first configurationincludes a first operating system, and wherein the second configurationincludes a second operating system.
 3. The method of claim 1, whereinthe first configuration includes a first version of a first operatingsystem, and wherein the second configuration includes a second versionof the first operating system.
 4. The method of claim 1, wherein thefirst configuration includes a first hardware architecture, and whereinthe second configuration includes a second hardware architecture.
 5. Themethod of claim 1, wherein the first configuration includes a firstalgorithm based on a first set of computing capabilities of the firstcompute node, and wherein the second configuration includes a secondalgorithm based on a second set of computing capabilities of the secondcompute node.
 6. The method of claim 1, wherein the first processingelement has a first application bundle which corresponds to the firstconfiguration, and wherein the second processing element has a secondapplication bundle which corresponds to the second configuration.
 7. Themethod of claim 1, further comprising: detecting a triggering eventrelated to the first compute node, wherein the first and secondprocessing elements are a same processing element.
 8. The method ofclaim 1, wherein the first processing element has a first applicationbundle which corresponds to the first configuration, further comprising:detecting a triggering event related to the first compute node;determining, based on the second configuration and the set of processingelements which includes the first processing element, to establish thefirst processing element on the second compute node; establishing thefirst processing element on the second compute node; and installing, onthe second compute node, a second application bundle which correspondsto the second configuration.
 9. The method of claim 1, furthercomprising: retrieving, by a first controller of the first compute node,a first application bundle; and installing, by the first controller ofthe first compute node, the first application bundle.
 10. The method ofclaim 1, further comprising: producing, by a compiler, a set ofapplication bundles for operation within both the first and secondconfigurations.
 11. The method of claim 10, wherein the set ofapplication bundles is one application bundle having configurations for:different operating systems, different versions of an operating system,and different hardware architectures.
 12. The method of claim 1, whereina first controller of the first compute node establishes the firstprocessing element on the first compute node without usage of a streamsmanager.
 13. The method of claim 1, wherein the structuring, thedetermining, and the establishing each occur in an automated fashionwithout user intervention.
 14. The method of claim 1, furthercomprising: receiving the stream of tuples to be processed by the set ofprocessing elements operating on the set of compute nodes; andprocessing, using the set of processing elements operating on the set ofcompute nodes, the stream of tuples.
 15. A system for managing a set ofheterogeneous compute nodes for processing a stream of tuples using aset of processing elements, the system comprising: a memory having a setof computer readable computer instructions, and a processor forexecuting the set of computer readable instructions, the set of computerreadable instructions including: structuring the set of compute nodes toinclude both a first compute node having a first configuration and asecond compute node having a second configuration, wherein the firstconfiguration differs from the second configuration; determining, by afirst controller on the first compute node, based on the firstconfiguration and the set of processing elements which includes a firstprocessing element, to establish the first processing element on thefirst compute node, wherein one or more stream operators are utilized toform one or more processing elements; establishing the first processingelement on the first compute node; determining, by a second controlleron the second compute node, based on the second configuration and theset of processing elements which includes a second processing element,to establish the second processing element on the second compute node;establishing the second processing element on the second compute node;associating the established first processing element with a firstapplication bundle corresponding to the first configuration and theestablished second processing element with a second application bundlecorresponding to the second configuration; submitting the associatedfirst processing element and associated second processing element to runon a specific compute node, wherein the specific compute node utilizes acontroller associated with the specific compute node and a plurality ofself-awareness information associated with the specific compute node todetermine an application bundle associated with the specific computenode to run a configuration associated with the specific compute node,wherein one or more processing elements is moved to one or moredifferent hosts as a stream computing application is running, whereineach of the different hosts from the one or more different hosts areassociated with two or more specific compute nodes; metering use of thefirst processing element and the second processing element; generatingan invoice based on comparing the metered use to a benchmark; andpresenting the generated invoice to a user.
 16. A computer programproduct for managing a set of heterogeneous compute nodes for processinga stream of tuples using a set of processing elements, the computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, wherein the computer readablestorage medium is not a transitory signal per se, the programinstructions executable by a processor to cause the processor to performa method comprising: structuring the set of compute nodes to includeboth a first compute node having a first configuration and a secondcompute node having a second configuration, wherein the firstconfiguration differs from the second configuration; determining, by afirst controller on the first compute node, based on the firstconfiguration and the set of processing elements which includes a firstprocessing element, to establish the first processing element on thefirst compute node, wherein one or more stream operators are utilized toform one or more processing elements; establishing the first processingelement on the first compute node; determining, by a second controlleron the second compute node, based on the second configuration and theset of processing elements which includes a second processing element,to establish the second processing element on the second compute node;establishing the second processing element on the second compute node;associating the established first processing element with a firstapplication bundle corresponding to the first configuration and theestablished second processing element with a second application bundlecorresponding to the second configuration; submitting the associatedfirst processing element and associated second processing element to runon a specific compute node, wherein the specific compute node utilizes acontroller associated with the specific compute node and a plurality ofself-awareness information associated with the specific compute node todetermine an application bundle associated with the specific computenode to run a configuration associated with the specific compute node,wherein one or more processing elements is moved to one or moredifferent hosts as a stream computing application is running, whereineach of the different hosts from the one or more different hosts areassociated with two or more specific compute nodes; metering use of thefirst processing element and the second processing element; generatingan invoice based on comparing the metered use to a benchmark; andpresenting the generated invoice to a user.
 17. The computer programproduct of claim 16, wherein at least one of: the program instructionsare stored in the computer readable storage medium in a data processingsystem, and wherein the program instructions were downloaded over anetwork from a remote data processing system; or the programinstructions are stored in the computer readable storage medium in aserver data processing system, and wherein the program instructions aredownloaded over a network to a remote data processing system for use inthe computer readable storage medium with the remote data processingsystem.